• Proceedings of the KSAR Conference
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• 2003.06a
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• pp.80-80
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• 2003

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms, accompanied by marked cell death in the striatum and cortex. Stereotaxic injection of quinolinic acid (QA) into striatum results in a degeneration of GABAergic neurons and exhibits abnormal motor behaviors typical of the illness. The objective of this study was carried out to obtain basic information about whether parthenogenetic mouse embryonic stem (PmES) cells are suitable for cell replacement therapy of HD. To establish PmES cell lines, hybrid F1 (C57BL/6xCBA/N) mouse oocytes were treated with 7% ethanol for 5 min and cytochalasin-B for 4 hr to initiate spontaneous cleavage. Thus established PmES cells were induced to differentiate using bFGF (20ng/ml) followed by selection of neuronal precursor cells for 8 days in N2 medium. After selection, cells were expanded at the presence of bFGF (20 ng/ml) for another 6 days, then a final differentiation step in N2 medium for 7 days. To establish recipient animal models of HD, young adult mice (7 weeks age ICR mice) were lesioned unilaterally with a stereotaxic injection of QA (60 nM) into the striatum and the rotational behavior of the animals was tested using apomorphine (0.1mg/kg, IP) 7 days after the induction of lesion. Animals rotating more than 120 turns per hour were selected and the differentiated PmES cells (1$\times$10$^4$cells/ul) were implanted into striatum. Four weeks after the graft, immunohistochemical studies revealed the presence of cells reactive to anti-NeuN antibody. However, only a slight improvement of motor behavior was observed. By Nissl staining, cell mass resembling tumor was found at the graft site and near cortex which may explain the slight behavioral improvement. Detailed experiment on cell viability, differentiation and migration explanted in vivo is currently being studied.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.420-423
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Journal of Power Electronics
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• v.11 no.2
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• pp.194-201
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• 2011

The air flow supplied to a fuel cell system is one of the most significant factors in determining fuel efficiency. The conventional method of controlling the air flow is to fix the oxygen supply at an estimated constant rate for optimal efficiency. However, the actual optimal point can deviated from the pre-set value due to temperature, load conditions and so on. In this paper, the maximum efficiency point tracking (MEPT) algorithm is proposed for finding the optimal air supply rate in real time to maximize the net-power generation of fuel cell systems. The fixed step MEPT algorithm has slow dynamics, thus it affects the overall efficiency. As a result, the variable step MEPT algorithm is proposed to compensate for this problem instead of a fixed one. The complete small signal model of a PEM Fuel cell system is developed to perform a stability analysis and to present a design guideline. For a design example, a 1kW PEM fuel cell system with a DSP 56F807 (Motorola Inc) was built and tested using the proposed MEPT algorithm. This control algorithm is very effective for a soft current change load like a grid connected system or a hybrid electric vehicle system with a secondary energy source.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.